Metal concentrations and metallothionein levels in Mytilus galloprovincialis from Elefsis bay (Saronikos gulf, Greece)

Spatial and temporal variability of Cd, Cu, Cr, Ni, Zn, Fe and Mn and metallothionein (MT) concentrations were determined in mussels Mytilus galloprovincialis from Elefsis bay (Saronikos gulf, Greece). Higher concentrations of both metal concentrations and MTs were recorded in mussels inhabiting industrial locations (steelworks and shipyard), indicating a markedly higher metal bioavailability. However at these sites, located at the eastern part of the bay, mussel metal concentrations were not always correlated with both seawater metal concentrations and MTs possibly due to different time scales of integration of the metal sources into mussels and/or the participation of other metal regulatory mechanisms except MT induction. The pattern of the temporal variation of mussel metal concentrations and the MT levels was similar among stations with higher values during the winter–spring season and lower during the summer–autumn period. The inverse relationship of flesh condition index with mussel metal concentrations was attributed to the influence of mussel annual reproductive cycle.     

Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m³ for PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.